Image-based object recognition processes are of importance for various driver-assistance systems in automated vehicles (e.g. partially automated or fully automated, i.e. autonomous) such as for lane-changing assistants, distance control systems, or pedestrian protection systems. A monocular image capturing device is a camera equipped with a single objective-lens that is fastened to the automated vehicle and which, for example, takes images of the traffic space in front of the automated vehicle. The automated vehicle which is equipped with the camera may also be referred to as a “host-vehicle”. Computer-assisted image processing systems are able to identify objects of interest such as road markings, traffic signs, pedestrians, and other vehicles with relatively high reliability in such camera images and optionally to track them by means of a tracking process.
A reliable estimate of the absolute distances to objects from the automated vehicle is desirable for safety-relevant systems in order to avoid collisions. Images taken or captured by monocular image capturing devices do not inherently contain reliably extractable depth information so that the determination of distance to an object is difficult when using only a monocular image capturing device. It has been suggested that an image processing system could derive the desired depth information from a priori information, for example by presuming that an object has a known width. It is, however, understood that such presumptions are frequently not met or are not met exactly enough in practice and that unacceptable errors can thereby enter into the associated calculation. Conventional processes for distance determination by means of a monocular image capturing device using a priori information are generally not suitable for safety-critical applications such as autonomous driving. The use of stereo cameras is a known means to determine distance, but is undesirable for cost reasons.